Biology subjects

Roark, C. L.

Publications and source records attributed to Roark, C. L..

3 recordsLinked to original sources

bioRxiv2024

Combining pupillometry and drift-diffusion models reveals auditory category learning dynamics

Pupillometry and drift-diffusion models (DDM) have emerged as powerful tools to offer insights into learning processes and decisional dynamics. Specifically, pupillary dilation can serve as a metric of arousal and cognitive processing, and DDMs examine processes underlying perceptual decision-making using behavioral accuracies and response times. Methodological constraints have complicated the combination of the two methods in the study of learning. DDMs require precise response times, yet pupillary responses are slow and are impacted by motor movements. Here, we developed a learning task that separately optimized measurement of behavioral response times and pupil dilation during learning. We modified a standard learning paradigm to include trials with and without timing delays between stimulus presentation and response. Delayed trials optimized measurement of the pupil, while immediate trials optimized behavioral response times. We tested whether decision-making processes estimated with DDMs could be recovered from delayed trials where pupil response measurement was optimized. Our results indicated that pupil responses on delayed trials showed distinct markers of learning that were not present on immediate trials. DDM parameters from delayed trials also showed expected trends for learning. Finally, we demonstrated that DDM parameters elicit differential pupillary responses. Together, these results indicate that learning dynamics and decisional processes can be decoded from pupillary responses.

physiology↗

bioRxiv2021

Separate neural dynamics underlying the acquisition of different auditory category structures

Current models of auditory category learning argue for a rigid specialization of hierarchically organized regions that are fine-tuned to extracting and mapping acoustic dimensions to categories. We test a competing hypothesis: the neural dynamics of emerging auditory representations are driven by category structures and learning strategies. We designed a category learning experiment where two groups of learners learned novel auditory categories with identical dimensions but differing category structures: rule-based (RB) and information-integration (II) based categories. Despite similar learning accuracies, strategies and cortico-striatal systems processing feedback differed across structures. Emergent neural representations of category information within an auditory frontotemporal pathway exclusively for the II learning task. In contrast, the RB task yielded neural representations within distributed regions involved in cognitive control that emerged at different time-points of learning. Our results demonstrate that learners neural systems are flexible and show distinct spatiotemporal patterns that are not dimension-specific but reflect underlying category structures. SignificanceWhether it is an alarm signifying danger or the characteristics of background noise, humans are capable of rapid auditory learning. Extant models posit that novel auditory representations emerge in the superior temporal gyrus, a region specialized for extracting behaviorally relevant auditory dimensions and transformed onto decisions via the dorsal auditory stream. Using a computational cognitive neuroscience approach, we offer an alternative viewpoint: emergent auditory representations are highly flexible, showing distinct spatial and temporal trajectories that reflect different category structures.

bioinformatics↗

bioRxiv2021

Working memory relates to individual differences in speech category learning: Insights from computational modeling and pupillometry

Across two experiments, we examine the relationship between individual differences in working memory (WM) and the acquisition of non-native speech categories in adulthood. While WM is associated with individual differences in a variety of learning tasks, successful acquisition of speech categories is argued to be contingent on WM-independent procedural-learning mechanisms. Thus, the role of WM in speech category learning is unclear. In Experiment 1, we show that individuals with higher WM acquire non-native speech categories faster and to a greater extent than those with lower WM. In Experiment 2, we replicate these results and show that individuals with higher WM use more optimal, procedural-based learning strategies and demonstrate more distinct speech-evoked pupillary responses for correct relative to incorrect trials. We propose that higher WM may allow for greater stimulus-related attention, resulting in more robust representations and optimal learning strategies. We discuss implications for neurobiological models of speech category learning.

neuroscience↗